Single-Cell RNA-Seq with Bioconductor in R

Analyze single-cell RNA-Seq data using normalization, dimensionality reduction, clustering and differential expression.

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Course Description

Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.

  1. 1

    What is Single-Cell RNA-Seq?


    In Chapter 1, you will learn what single-cell RNA-Seq is and why it is a such a powerful technique. By the end of this chapter, you'll also know how to load, create, and access single-cell datasets in R.

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    What is Single Cell RNA-Seq, and why is it useful?
    50 xp
    Bulk versus Single-cell RNA-Seq
    50 xp
    Explore a toy scRNA-Seq dataset
    100 xp
    Compute cell coverage
    100 xp
    Typical workflow
    50 xp
    GC content
    100 xp
    Library size
    50 xp
    Nesting between batches and biology
    100 xp
    Load, create, and access single-cell datasets in R
    50 xp
    SCE object from counts matrix
    100 xp
    SCE object from SummarizedExperiment
    100 xp
    Load a single-cell dataset in R
    100 xp
  2. 2

    Quality Control and Normalization

    In Chapter 2, we go over the first steps of the workflow to analyze single-cell RNA-seq data, which include quality control and normalization. These two steps should get all the technical issues and biases out of the way so that in the next chapters we can focus on the biological signal of interest.

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  3. 3

    Visualization and Dimensionality Reduction

    When studying single-cell data at the cellular level, the number of dimensions is the number of genes. The goal of dimensionality reduction is to reduce the number of dimensions to a smaller number either to visualize the data in 2 dimensions or to prepare the dataset for subsequent steps like clustering.

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  4. 4

    Cell Clustering and Differential expression analysis

    In Chapter 4, we cluster cells with similar gene expression profiles and then perform differential expression (DE) analysis to find genes differentially expressed between known groups of cells. We then visualize DE genes with volcano plots and heatmaps.

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Mouse Epithelium datasetToy datasetTung dataset


David CamposRichie CottonShon Inouye
Fanny Perraudeau Headshot

Fanny Perraudeau

Senior Data Scientist, Whole Biome

Fanny Perraudeau is a Senior Data Scientist at Whole Biome where she manages, designs, and implements novel genomics algorithms and bioinformatics pipelines to further improve the analysis of of Whole Biome microbiome data. In addition, she runs statistical analyses to aid the company’s therapeutic discovery efforts. She has a master from Ecole Polytechnique, France and a PhD in Biostatistics from University of California, Berkeley with a Designated Emphasis in Computational and Genomic Biology. Much of her work is motivated by the development and application of statistical methods and software for the analysis of biomedical and genomic data, especially metagenomics and single-cell RNASeq.
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